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40
MAXIMUM ENTROPY MODEL FOR PUNCTUATION ANNOTATION FROM SPEECH
"... In this paper we develop a maximum-entropy based method for annotating spontaneous conversational speech with punctuation. The goal of this task is to make automatic transcriptions more readable by humans, and to render them into a form that is useful for subsequent natural language processing and d ..."
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Cited by 65 (0 self)
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In this paper we develop a maximum-entropy based method for annotating spontaneous conversational speech with punctuation. The goal of this task is to make automatic transcriptions more readable by humans, and to render them into a form that is useful for subsequent natural language processing and discourse analysis. Our basic approach is to view the insertion of punctuation as a form of tagging, in which words are tagged with appropriate punctuation, and to apply a maximum entropy tagger that uses both lexical and prosodic features. We present experimental results on Switchboard data with both reference transcriptions and transcriptions produced by a speech recognition system.
Using conditional random fields for sentence boundary detection in speech
- In Proceedings of the 43rd Annula Meeting of the ACL
, 2005
"... Sentence boundary detection in speech is important for enriching speech recognition output, making it easier for humans to read and downstream modules to process. In previous work, we have developed hidden Markov model (HMM) and maximum entropy (Maxent) classifiers that integrate textual and prosodi ..."
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Cited by 37 (6 self)
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Sentence boundary detection in speech is important for enriching speech recognition output, making it easier for humans to read and downstream modules to process. In previous work, we have developed hidden Markov model (HMM) and maximum entropy (Maxent) classifiers that integrate textual and prosodic knowledge sources for detecting sentence boundaries. In this paper, we evaluate the use of a conditional random field (CRF) for this task and relate results with this model to our prior work. We evaluate across two corpora (conversational telephone speech and broadcast news speech) on both human transcriptions and speech recognition output. In general, our CRF model yields a lower error rate than the HMM and Maxent models on the NIST sentence boundary detection task in speech, although it is interesting to note that the best results are achieved by three-way voting among the classifiers. This probably occurs because each model has different strengths and weaknesses for modeling the knowledge sources. 1
Comparing and Combining Generative and Posterior Probability Models: Some advances in sentence boundary detection in speech
- IN PROC. OF EMNLP
, 2004
"... We compare and contrast two different models for detecting sentence-like units in continuous speech. The first approach uses hidden Markov sequence models based on N-grams and maximum likelihood estimation, and employs model interpolation to combine different representations of the data. The second ..."
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Cited by 23 (13 self)
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We compare and contrast two different models for detecting sentence-like units in continuous speech. The first approach uses hidden Markov sequence models based on N-grams and maximum likelihood estimation, and employs model interpolation to combine different representations of the data. The second approach models the posterior probabilities of the target classes; it is discriminative and integrates multiple knowledge sources in the maximum entropy (maxent) framework. Both models combine lexical, syntactic, and prosodic information. We develop a technique for integrating pretrained probability models into the maxent framework, and show that this approach can improve on an HMM-based state-of-the-art system for the sentence-boundary detection task. An even more substantial improvement is obtained by combining the posterior probabilities of the two systems.
Improving automatic sentence boundary detection with confusion networks.
- In Proc. HLT-NAACL,
, 2004
"... Abstract We extend existing methods for automatic sentence boundary detection by leveraging multiple recognizer hypotheses in order to provide robustness to speech recognition errors. For each hypothesized word sequence, an HMM is used to estimate the posterior probability of a sentence boundary at ..."
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Cited by 17 (11 self)
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Abstract We extend existing methods for automatic sentence boundary detection by leveraging multiple recognizer hypotheses in order to provide robustness to speech recognition errors. For each hypothesized word sequence, an HMM is used to estimate the posterior probability of a sentence boundary at each word boundary. The hypotheses are combined using confusion networks to determine the overall most likely events. Experiments show improved detection of sentences for conversational telephone speech, though results are mixed for broadcast news.
RESTORING PUNCTUATION AND CAPITALIZATION IN TRANSCRIBED SPEECH
"... Adding punctuation and capitalization greatly improves the readability of automatic speech transcripts. We discuss an approach for performing both tasks in a single pass using a purely text-based n-gram language model. We study the effect on performance of varying the n-gram order (from n = 3to n = ..."
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Cited by 16 (0 self)
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Adding punctuation and capitalization greatly improves the readability of automatic speech transcripts. We discuss an approach for performing both tasks in a single pass using a purely text-based n-gram language model. We study the effect on performance of varying the n-gram order (from n = 3to n = 6) and the amount of training data (from 58 million to 55 billion tokens). Our results show that using larger training data sets consistently improves performance, while increasing the n-gram order does not help nearly as much. Index Terms — Speech recognition, punctuation, capitalization. 1.
Automatic Detection and Classification of Prosodic Events
, 2009
"... Prosody, or intonation, is a critically important component of spoken communication. The automatic extraction of prosodic information is necessary for machines to process speech with human levels of proficiency. In this thesis we describe work on the automatic detection and classification of prosodi ..."
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Cited by 15 (4 self)
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Prosody, or intonation, is a critically important component of spoken communication. The automatic extraction of prosodic information is necessary for machines to process speech with human levels of proficiency. In this thesis we describe work on the automatic detection and classification of prosodic events – specifically, pitch accents and prosodic phrase boundaries. We present novel techniques, feature representations and state of the art performance in each of these tasks. We also present three proof-of-concept applications – speech summarization, story segmentation and non-native speech assessment – showing that access to hypothesized prosodic event information can be used to improve the performance of downstream spoken language processing tasks. We believe the contributions of this thesis advance the understanding of prosodic events and the use of prosody in spoken language processing towards the goal of human-like processing of speech by machines.
A Study in Machine Learning from Imbalanced Data for Sentence Boundary Detection in Speech
- Computer Speech and Language
, 2006
"... Enriching speech recognition output with sentence boundaries improves its human readability and enables further processing by downstream language processing modules. We have constructed a hidden Markov model (HMM) system to detect sentence boundaries that uses both prosodic and textual information. ..."
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Cited by 13 (5 self)
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Enriching speech recognition output with sentence boundaries improves its human readability and enables further processing by downstream language processing modules. We have constructed a hidden Markov model (HMM) system to detect sentence boundaries that uses both prosodic and textual information. Since there are more nonsentence boundaries than sentence boundaries in the data, the prosody model, which is implemented as a decision tree classifier, must be constructed to effectively learn from the imbalanced data distribution. To address this problem, we investigate a variety of sampling approaches and a bagging scheme. A pilot study was carried out to select methods to apply to the full NIST sentence boundary evaluation task across two corpora (conversational telephone speech and broadcast news speech), using both human transcriptions and recognition output. In the pilot study, when classification error rate is the performance measure, using the original training set achieves the best performance among the sampling methods, and an ensemble of multiple classifiers from different downsampled training sets achieves
Maximum Entropy Segmentation of Broadcast News
- in Proceedings of ICASSP 2005
, 2005
"... This paper presents an automatic system for structuring and preparing a news broadcast for applications such as speech summarization, browsing, archiving and information retrieval. This process comprises transcribing the audio using an automatic speech recognizer and subsequently segmenting the text ..."
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Cited by 11 (1 self)
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This paper presents an automatic system for structuring and preparing a news broadcast for applications such as speech summarization, browsing, archiving and information retrieval. This process comprises transcribing the audio using an automatic speech recognizer and subsequently segmenting the text into utterances and topics. A maximum entropy approach is used to build statistical models for both utterance and topic segmentation. The experimental work addresses the effect on performance of the topic boundary detector of three factors: the information sources used, the quality of the ASR transcripts, and the quality of the utterance boundary detector. The results show that the topic segmentation is not affected severely by transcripts errors, whereas errors in the utterance segmentation are more devastating.
A cascaded broadcast news highlighter
- IEEE Transactions on Audio, Speech and Language Processing
, 2008
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